{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "70446512-3807-4694-a784-97300a476423",
   "metadata": {},
   "source": [
    "# Structure generation and NEP training\n",
    "\n",
    "This tutorial demonstrates a simple approach for generating an initial set of training structures for the construction of a NEP for bulk aluminium."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242c0306",
   "metadata": {},
   "source": [
    "## Structure generation\n",
    "First, we identify all the relevant prototype structures (FCC, BCC, HCP, diamond) that we want to include in the training set.\n",
    "\n",
    "Next, we generate strained and deformed versions of the prototype structures.\n",
    "Additionally, we include rattled supercells (of various sizes) with random atomic displacements.\n",
    "\n",
    "Lastly, we note that if one wants to consider mixed alloy systems, defects, surfaces etc, one needs to be more careful during structure generation to include training structures such that one spans that part of the configurational space.\n",
    "\n",
    "## Training\n",
    "We use the function `setup_training` in calorine in order to set up and prepare all relevant files for the training.\n",
    "Here, we use the [ASE EMT calculator](https://wiki.fysik.dtu.dk/ase/ase/calculators/emt.html) (instead of density functional theory) to compute energies, forces and stresses for the training structures to make the tutorial more lightweight.\n",
    "After the NEP is trained we carry out some simple analysis of the model, specifically by constructing parity plots.\n",
    "\n",
    "Note that the approach in this tutorial can be used to construct an initial NEP for the given system.\n",
    "However, after training this initial model it is recommened to carry out a few iterations of active learning based on, e.g., molecular dynamics simulations to enhance the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2192907-c952-4773-9ea0-3d3221d4c0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from ase.build import bulk, make_supercell, surface\n",
    "from ase.calculators.emt import EMT\n",
    "from ase.io import write\n",
    "from calorine.tools import relax_structure\n",
    "from hiphive.structure_generation import generate_mc_rattled_structures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a7b3d2-a627-41ec-b33a-228c51d3037e",
   "metadata": {},
   "source": [
    "## Prototype structures\n",
    "First, we collect all prototype structures of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f530919-e703-4724-9839-2b65cab42b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "prototype_structures = {}\n",
    "prototype_structures['fcc'] = bulk('Al', crystalstructure='fcc', a=4.05, cubic=True)\n",
    "prototype_structures['bcc'] = bulk('Al', crystalstructure='bcc', a=3.12, cubic=True)\n",
    "prototype_structures['hcp'] = bulk('Al', crystalstructure='hcp', a=2.82, c=4.47)\n",
    "prototype_structures['diamond'] = bulk('Al', crystalstructure='diamond', a=6.1, cubic=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec53920-9bd3-4423-800e-9cc5fa0badf1",
   "metadata": {},
   "source": [
    "## Generation of strained and deformed prototype structures\n",
    "Next, we generate strained and deformed versions of prototype structures.\n",
    "`strain_lim` sets the range on how much we can strain or deform the cell of prototype structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2045075-c281-41ac-a0d9-b9e98e5ecdfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training structures: 120\n"
     ]
    }
   ],
   "source": [
    "def generate_strained_structure(prim, strain_lim):\n",
    "    strains = np.random.uniform(*strain_lim, (3, ))\n",
    "    atoms = prim.copy()\n",
    "    cell_new = prim.cell[:] * (1 + strains)\n",
    "    atoms.set_cell(cell_new, scale_atoms=True)\n",
    "    return atoms\n",
    "\n",
    "\n",
    "def generate_deformed_structure(prim, strain_lim):\n",
    "    R = np.random.uniform(*strain_lim, (3, 3))\n",
    "    M = np.eye(3) + R\n",
    "    atoms = prim.copy()\n",
    "    cell_new = M @ atoms.cell[:]\n",
    "    atoms.set_cell(cell_new, scale_atoms=True)\n",
    "    return atoms\n",
    "\n",
    "\n",
    "# parameters\n",
    "strain_lim = [-0.05, 0.05]\n",
    "n_structures = 15\n",
    "\n",
    "training_structures = []\n",
    "for name, prim in prototype_structures.items():\n",
    "    for it in range(n_structures):\n",
    "        prim_strained = generate_strained_structure(prim, strain_lim)\n",
    "        prim_deformed = generate_deformed_structure(prim, strain_lim)\n",
    "        \n",
    "        training_structures.append(prim_strained)\n",
    "        training_structures.append(prim_deformed)\n",
    "\n",
    "print('Number of training structures:', len(training_structures))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43db013e-18ff-46cb-b7b0-041b32dfe8eb",
   "metadata": {},
   "source": [
    "## Generation of rattled structures\n",
    "We also include some rattled structures in the training set. One can use the functions `generate_rattled_structures` (for small displacements) and `generate_mc_rattled_structures` for larger displacements from `hiphive`, see the [hiphive documentation](https://hiphive.materialsmodeling.org/tutorial/prepare_reference_data.html#structure-generation) for more details.\n",
    "\n",
    "The `d_min` parameter approximatley corresponds to the smallest interatomic distance we would like to see in the MC-rattled structures, here set to about 90% of the nearest neighbor distance.\n",
    "\n",
    "The `rattle_std` and `n_iter` determines how large the displacements will, the displacements generated will roughly be `n_iter**0.5 * rattle_std`.\n",
    "\n",
    "Note that it can be useful to include supercells of various system sizes as done below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c39dc0bf-245e-432a-9e2a-84aab42ed526",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_structures = 5\n",
    "rattle_std = 0.04\n",
    "d_min = 2.3\n",
    "n_iter = 20\n",
    "\n",
    "size_vals = {}\n",
    "size_vals['fcc'] = [(2, 2, 2), (3, 3, 3), (4, 4, 4)]\n",
    "size_vals['bcc'] = [(2, 2, 2), (3, 3, 3), (4, 4, 4)]\n",
    "size_vals['hcp'] = [(2, 2, 2), (3, 3, 3), (4, 4, 4)]\n",
    "size_vals['diamond'] = [(2, 2, 2), (3, 3, 3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28e7d477-161d-42b9-8e50-cfd15a67246e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fcc, size (2, 2, 2), natoms 32,  volume 17.545\n",
      "fcc, size (2, 2, 2), natoms 32,  volume 16.576\n",
      "fcc, size (2, 2, 2), natoms 32,  volume 17.164\n",
      "fcc, size (2, 2, 2), natoms 32,  volume 15.687\n",
      "fcc, size (2, 2, 2), natoms 32,  volume 16.386\n",
      "fcc, size (3, 3, 3), natoms 108,  volume 17.944\n",
      "fcc, size (3, 3, 3), natoms 108,  volume 17.985\n",
      "fcc, size (3, 3, 3), natoms 108,  volume 16.193\n",
      "fcc, size (3, 3, 3), natoms 108,  volume 16.336\n",
      "fcc, size (3, 3, 3), natoms 108,  volume 16.648\n",
      "fcc, size (4, 4, 4), natoms 256,  volume 16.636\n",
      "fcc, size (4, 4, 4), natoms 256,  volume 18.020\n",
      "fcc, size (4, 4, 4), natoms 256,  volume 17.754\n",
      "fcc, size (4, 4, 4), natoms 256,  volume 16.065\n",
      "fcc, size (4, 4, 4), natoms 256,  volume 15.419\n",
      "bcc, size (2, 2, 2), natoms 16,  volume 14.782\n",
      "bcc, size (2, 2, 2), natoms 16,  volume 14.768\n",
      "bcc, size (2, 2, 2), natoms 16,  volume 15.874\n",
      "bcc, size (2, 2, 2), natoms 16,  volume 15.626\n",
      "bcc, size (2, 2, 2), natoms 16,  volume 13.882\n",
      "bcc, size (3, 3, 3), natoms 54,  volume 15.271\n",
      "bcc, size (3, 3, 3), natoms 54,  volume 13.676\n",
      "bcc, size (3, 3, 3), natoms 54,  volume 16.556\n",
      "bcc, size (3, 3, 3), natoms 54,  volume 14.940\n",
      "bcc, size (3, 3, 3), natoms 54,  volume 15.441\n",
      "bcc, size (4, 4, 4), natoms 128,  volume 16.244\n",
      "bcc, size (4, 4, 4), natoms 128,  volume 14.433\n",
      "bcc, size (4, 4, 4), natoms 128,  volume 16.276\n",
      "bcc, size (4, 4, 4), natoms 128,  volume 15.721\n",
      "bcc, size (4, 4, 4), natoms 128,  volume 14.219\n",
      "hcp, size (2, 2, 2), natoms 16,  volume 16.084\n",
      "hcp, size (2, 2, 2), natoms 16,  volume 16.415\n",
      "hcp, size (2, 2, 2), natoms 16,  volume 15.554\n",
      "hcp, size (2, 2, 2), natoms 16,  volume 15.406\n",
      "hcp, size (2, 2, 2), natoms 16,  volume 16.846\n",
      "hcp, size (3, 3, 3), natoms 54,  volume 15.968\n",
      "hcp, size (3, 3, 3), natoms 54,  volume 17.227\n",
      "hcp, size (3, 3, 3), natoms 54,  volume 16.343\n",
      "hcp, size (3, 3, 3), natoms 54,  volume 15.922\n",
      "hcp, size (3, 3, 3), natoms 54,  volume 15.945\n",
      "hcp, size (4, 4, 4), natoms 128,  volume 16.196\n",
      "hcp, size (4, 4, 4), natoms 128,  volume 15.069\n",
      "hcp, size (4, 4, 4), natoms 128,  volume 14.559\n",
      "hcp, size (4, 4, 4), natoms 128,  volume 14.829\n",
      "hcp, size (4, 4, 4), natoms 128,  volume 15.930\n",
      "diamond, size (2, 2, 2), natoms 64,  volume 27.739\n",
      "diamond, size (2, 2, 2), natoms 64,  volume 30.554\n",
      "diamond, size (2, 2, 2), natoms 64,  volume 26.404\n",
      "diamond, size (2, 2, 2), natoms 64,  volume 31.200\n",
      "diamond, size (2, 2, 2), natoms 64,  volume 26.451\n",
      "diamond, size (3, 3, 3), natoms 216,  volume 28.871\n",
      "diamond, size (3, 3, 3), natoms 216,  volume 31.406\n",
      "diamond, size (3, 3, 3), natoms 216,  volume 29.147\n",
      "diamond, size (3, 3, 3), natoms 216,  volume 25.816\n",
      "diamond, size (3, 3, 3), natoms 216,  volume 30.266\n"
     ]
    }
   ],
   "source": [
    "for name, prim in prototype_structures.items():\n",
    "    for size in size_vals[name]:\n",
    "        for it in range(n_structures):\n",
    "            supercell = generate_strained_structure(prim.repeat(size), strain_lim)\n",
    "            rattled_supercells = generate_mc_rattled_structures(supercell, n_structures=1, rattle_std=rattle_std, d_min=d_min, n_iter=n_iter)\n",
    "            print(f'{name}, size {size}, natoms {len(supercell)},  volume {supercell.get_volume() / len(supercell):.3f}')\n",
    "            training_structures.extend(rattled_supercells)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6230944",
   "metadata": {},
   "source": [
    "We can visualize and inspect the training structures using the `view` function in ASE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4053822c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training structures: 175\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<subprocess.Popen at 0x7f9cac51bbe0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ase.visualize import view\n",
    "\n",
    "print('Number of training structures:', len(training_structures))\n",
    "\n",
    "view(training_structures)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "148d8df1",
   "metadata": {},
   "source": [
    "## Training\n",
    "We use the EMT calculator to compute the reference energies, forces and stresses.\n",
    "We change some of the default NEP hyper-parameters, such as the number of neurons to 30.\n",
    "\n",
    "Note that we only train for 100,000 generations.\n",
    "In practice one should train until the model has converged, which typically requires many more generations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e869f512",
   "metadata": {},
   "outputs": [],
   "source": [
    "# do reference calculations\n",
    "for atoms in training_structures:\n",
    "    atoms.calc = EMT()\n",
    "    atoms.get_potential_energy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e1e597d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from calorine.nep import setup_training\n",
    "\n",
    "# prepare input for NEP construction\n",
    "parameters = dict(version=4,\n",
    "                  type=[1, 'Al'],\n",
    "                  cutoff=[8, 4],\n",
    "                  neuron=30,\n",
    "                  generation=100000,\n",
    "                  batch=1000000)\n",
    "\n",
    "setup_training(parameters, training_structures, rootdir='model', overwrite=True, n_splits=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09cf6c36",
   "metadata": {},
   "source": [
    "The cell above sets up all required files for training in the `model/` directory. In `model/nepmodel_full` all structures are used for training, and in the `model/nepmodel_split` directories 90% of the structures are used for training and 10% for testing, which is known as [K-fold cross validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) with `K=10`.\n",
    "\n",
    "Next, one needs to go into each model directory (`model/nepmodel_full` and `model/nepmodel_split*`) and run `nep` in order to start the training. The training is computationally expensive and here with about 200 training structures takes about 2 hours on a A100 GPU."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2910062",
   "metadata": {},
   "source": [
    "## Analysis of the model\n",
    "First we plot the total loss function, the L1 and L2 regularization part of the loss function and the corresponding RMSE (root mean squared error) for the full training set of the  `nepmodel_full` model.\n",
    "\n",
    "We can see that the training has more or less converged with 100,000 generations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "661da981",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from ase.units import GPa\n",
    "from calorine.nep import get_parity_data, read_loss, read_structures\n",
    "from matplotlib import pyplot as plt\n",
    "from pandas import DataFrame, concat as pd_concat\n",
    "from sklearn.metrics import r2_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b39343a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = read_loss('model/nepmodel_full/loss.out')\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(6.0, 4.0), nrows=2,sharex=True, dpi=120)\n",
    "\n",
    "ax = axes[0]\n",
    "ax.set_ylabel('Loss')\n",
    "ax.plot(loss.total_loss, label='total')\n",
    "ax.plot(loss.L2, label='$L_2$')\n",
    "ax.plot(loss.L1, label='$L_1$')\n",
    "ax.set_yscale('log')\n",
    "ax.legend()\n",
    "\n",
    "ax = axes[1]\n",
    "ax.plot(loss.RMSE_F_train, label='forces (eV/Å)')\n",
    "ax.plot(loss.RMSE_V_train, label='virial (eV/atom)')\n",
    "ax.plot(loss.RMSE_E_train, label='energy (eV/atom)')\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('Generation')\n",
    "ax.set_ylabel('RMSE')\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(hspace=0, wspace=0)\n",
    "fig.align_ylabels(axes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd69dac",
   "metadata": {},
   "source": [
    "Finally, we plot the parity plots using the `read_structures` and `get_parity_data` functions.\n",
    "We can see that the model does a good job of reproducing the reference data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f74d388d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x312 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_structures, _ = read_structures('model/nepmodel_full')\n",
    "\n",
    "units = dict(\n",
    "    energy='eV/atom',\n",
    "    force='eV/Å',\n",
    "    virial='eV/atom',\n",
    "    stress='GPa',\n",
    ")\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(9.0, 2.6), ncols=4, dpi=120)\n",
    "kwargs = dict(alpha=0.2, s=0.5)\n",
    "\n",
    "for icol, (prop, unit) in enumerate(units.items()):\n",
    "    df = get_parity_data(training_structures, prop, flatten=True)\n",
    "    R2 = r2_score(df.target, df.predicted)\n",
    "    rmse = mean_squared_error(df.target, df.predicted, squared=False)\n",
    "\n",
    "    ax = axes[icol]\n",
    "    ax.set_xlabel(f'Target {prop} ({unit})')\n",
    "    ax.set_ylabel(f'Predicted ({unit})')\n",
    "    ax.scatter(df.target, df.predicted, **kwargs)\n",
    "    ax.set_aspect('equal')\n",
    "    mod_unit = unit.replace('eV', 'meV').replace('GPa', 'MPa')\n",
    "    ax.text(0.1, 0.75, f'{1e3*rmse:.1f} {mod_unit}\\n' + '$R^2= $' + f' {R2:.5f}', transform=ax.transAxes)\n",
    "\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
